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Page 105
... filter . By way of comparison , six 1 - D Gaussian filters of similar extent to the corresponding matched filters were also applied to the image . The dotted curve of Figure ( 11 ) describes the localization obtained from the Gaussian ...
... filter . By way of comparison , six 1 - D Gaussian filters of similar extent to the corresponding matched filters were also applied to the image . The dotted curve of Figure ( 11 ) describes the localization obtained from the Gaussian ...
Page 106
... Filter Width ( pixels ) Figure 11 : Location error ( root of variance ) for Gaussian ( + ) and " matched filter " ( x ) shown as a function of the total filter width . 140 As in the one - dimensional case , edges must be separate enough ...
... Filter Width ( pixels ) Figure 11 : Location error ( root of variance ) for Gaussian ( + ) and " matched filter " ( x ) shown as a function of the total filter width . 140 As in the one - dimensional case , edges must be separate enough ...
Page 109
... filter for edge detection based on the one step edge and multi - edge models , and the DRF method is proposed . The performance of this method is analysed and compared with the Laplacian - Gaussian filter theo- retically and ...
... filter for edge detection based on the one step edge and multi - edge models , and the DRF method is proposed . The performance of this method is analysed and compared with the Laplacian - Gaussian filter theo- retically and ...
Contents
Depth from Three Camera Stereo | 2 |
The Calibration Problem for Stereo | 15 |
Model Based Analysis of Industrial Scenes | 28 |
Copyright | |
41 other sections not shown
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affine transform algorithm analysis angle applied approach approximation array Artificial Intelligence axis binary boundary calibration camera clustering component Computer Vision connected constraints contour convolution coordinate system corresponding curvature curve defined derivative described detector determined direction edge detection elements equation error estimate filter function Gaussian geometric given gradient histogram Hough transform IEEE IEEE Trans image plane Image Processing implementation input label line segments linear machine machine vision matching matrix measure merging method motion node noise object obtained octree operations optical flow orientation output parallel parameters Pattern Recognition perspective projection pixel planar polygon problem Proc procedure processors projection quadtree region representation rotation scene shape SIMD smoothing solution space step stereo structure surface surface normal technique template tensor texture Theorem threshold tion transformation tree values vector visual window zero-crossings